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Viewpoints: When Social Ranking Meets the Semantic Web Proceedings of the Thirtieth International Florida Artificial Intelligence Research Society Conference ViewpointS: When Social Ranking Meets the Semantic Web Philippe Lemoisson,1 Guillaume Surroca,2 Clément Jonquet,2 Stefano A. Cerri2 1UMR Territories, Environment, Remote Sensing and Spatial Information, CIRAD, Montpellier, France 2Laboratory of Informatics Robotics Micro-Electronics of Montpellier, France [email protected];{surroca, jonquet, cerri}@lirmm.fr Abstract and Apostolou 2006). The frontier between them has nev- Reconciling the ecosystem of semantic Web data with the ertheless become porous: on one hand, the Web 2.0 con- ecosystem of social Web participation has been a major is- tributors attempt to explore more and more Web data be- sue for the Web Science community. To answer this need, fore posting comments and ranking contents; on the other we propose an innovative approach called ViewpointS hand the data analysis algorithms start to integrate social where the knowledge is topologically, rather than logically, micro-contributions to improve their results. Bridging the explored and assessed. Both social contributions and linked gap between social ranking and the semantic Web is a big data are represented by triples agent-resource-resource called “viewpoints”. A “viewpoint” is the subjective decla- issue: it might be the key of a collective knowledge system ration by an agent (human or artificial) of some semantic able to bring in new levels of understanding in the sense proximity between two resources. Knowledge resources and coined by Gruber in (Gruber 2008). viewpoints form a bipartite graph called “knowledge The current paper is a proof of concept for an approach graph”. Information retrieval is processed on demand by stepping forward in this direction called ViewpointS. After choosing a user’s “perspective” i.e., rules for quantifying and aggregating “viewpoints” which yield a “knowledge presenting our major sources of inspiration within the state map”. This map is equipped with a topology: the more of the art, we propose a formalism supporting the storage viewpoints between two given resources, the shorter the dis- and exploitation of knowledge, and detail a topological tance; moreover, the distances between resources evolve assessment of information. Then we experiment all the along time according to new viewpoints, in the metaphor of novel aspects of our approach with the MovieLens dataset. synapses’ strengths. Our hypothesis is that these dynamics actualize an adaptive, actionable collective knowledge. Finally, we discuss the formalism and measurements and We test our hypothesis with the MovieLens dataset by briefly expose directions for future work. showing the ability of our formalism to unify the semantics issued from linked data e.g., movies’ genres and the social Web e.g., users’ ratings. Moreover, our results prove the Related work and inspirations relevance of the topological approach for assessing and comparing along the time the respective powers of ‘genres’ Facing the issue of bridging the gap between social ranking and ‘ratings’ for recommendation. and the semantic Web, (Breslin, Passant, and Vrandečić 2011) distinguish two main options in the literature con- sisting respectively in “post-formalizing” and “pre- Introduction formalizing” the informal content of the social Web. Since the last decade, reconciling the ecosystem of seman- The ViewpointS approach takes a third option: to repre- tic Web data with the ecosystem of social Web participa- sent both social contributions and linked data by triples tion has been a major issue for the Web Science communi- agent-resource-resource. The major consequence of this ty (Hendler et al. 2008). Each ecosystem has its own dy- choice of a memory built upon connections made by agents namics and challenges: the countless subjective contribu- is to let the collected knowledge evolve continuously along tions of the social Web yield folksonomies, but without the interactions of the contributors. Another consequence is centrally controlled coherence (Mika 2007; Mikroyannidis to ensure trustworthiness, well in line with the recent shift 2007) whereas semantically linking datasets whose models in the Web paradigm bringing the contributor back in the evolve independently is a never ending task (Karapiperis loop (O’Reilly 2015). In ViewpointS, we started from a tripartite model agent- resource-tag generalizing social networks such as Flickr Copyright © 2017, Association for the Advancement of Artificial Intelli- and proposed as building block the more abstract triple gence (www.aaai.org). All rights reserved. agent-resource-resource, in which agents may themselves 329 be resources, expressing: “the belief of a resource/agent that two resources are close”. This yields a topological, /07-'&)'2'33052%' rather than logical, exploitation of the “wisdom of the crowd” in a manner similar to (Mika 2007; Markines et al. 2009; Specia and Motta 2007). Taking inspiration from the contexts of annotation, disambiguation, concept alignment 5.#/ )'/4 5.'2+% 02')#- '230/ 0%5.'/4 and information retrieval (Harispe et al. 2014; Lee et al. 2008), we then grouped the triples in order to yield ‘seman- '3%2+1402 tic similarity’ or ‘semantic proximity’. We firstly aggregate 24+(+%+#- *83+%#- all the triples connecting two given knowledge resources )'/4 0%5.'/4 into a higher level binary link called a synapse. In a second step, we made the hypothesis that any user's context could Figure 1: Knowledge resources be translated into a set of quantification rules for the view- • Human Agent or Legal person: entity “performing acts points called a perspective; this is in line with (Kim and and undertaking obligations” [32] such as humans or or- Scerri 2008) which advice to evaluate ontologies on de- ganizations, by emitting connections between resources. mand with respect to a specific context. Once a perspective • Artificial Agent: numeric entity emitting connections is adopted, the initial heterogeneous semantics carried by between resources. the viewpoints are transformed into a knowledge map • Physical document: document of the real world such as a equipped with a distance. As a consequence, the agents can book. use the proximities resulting from the existing viewpoints • Numeric document: numeric entity such as a Web page. when browsing knowledge maps and reversely update the • Descriptor: meaningful linguistic expression, playing the knowledge through new viewpoints expressing their feed- role of tag. back e.g., “I believe this agent matches/does-not-match the Knowledge resources participate to connections called topic of my query” or “I deny concept-a/ instance-1 and viewpoints: each viewpoint is a subjective connection es- concept-b/ instance-2 are connected through relation-r”. tablished by an agent (Human or Artificial) between two Along these exploitation/feedback cycles, the shared knowledge resources; each viewpoint implicitly brings in knowledge is continuously elicited against the beliefs of the specific semantics of its emitter. However, these se- the agents in a selection process supported by the evolving mantics do not need to be shared by the agents that will strength of synapses. Here comes the metaphor of the further exploit the knowledge, as it will be explained when brain. According to the Theory of Neuronal Group Selec- describing the “knowledge maps”. The formalization of tion (Edelman and Tononi 2000), which recently re- these heterogeneous semantics is illustrated in Figure 2: appeared in the front scene under the name of connectome (Seung 2012), knowledge results from a process of contin- /07-'&)' ual re-categorization. In our approach, the dynamics of 2'33052%' synapses aim at yielding an evolving topology where knowledge is constantly reorganized1. /07-'&)' )'/4 T W The next section firstly presents the formalism support- 2'33052%' ing the ViewpointS “Knowledge graph”, then goes through 6+'710+/4 some aspects exploring its topology in order to assess col- lective knowledge on top of heterogeneous semantics. Figure 2: A “viewpoint”. The straight arrow gives the prove- nance; ‘T’ gives the semantics, ‘τ’ gives the time stamp A topological, unified view on heterogeneous The viewpoint (a1, {r2, r3}, T, τ) stands for: the agent a1 semantics believes at time τ that r2 and r3 are related according to the semantics carried by T. For instance: In the ViewpointS approach, all the resources (identified The viewpoint (AA-MovieLens, {movieX, genreY}, by a URI) contributing to knowledge are grouped in a mo:genre, τ1) stands for: the artificial agent ‘AAMov- single class: Knowledge resource. Figure 1 illustrates five ieLens’ declares at τ1 that the physical document ‘movieX’ mutually exclusive subclasses of knowledge resources matches the descriptor ‘genreY’; in this example, covering most of the practical cases: T=mo:genre is a standard unambiguous semantic Web property issued the MovieLens linked data. The viewpoint (userX, {movieY, ***}, vp:rating, τ2) stands for: the legal person ‘userX’ declares at τ2 that the 1 This point requires a real-life scenario and cannot be demonstrated in physical document ‘movieY’ matches the descriptor ‘*** = this paper; setting such a scenario is our next objective. 330 of average interest’; in that example, T=vp:rating is a tran- ogy of which can be exploited with standard graph algo- scription of the MovieLens linked data. rithms in order to exhibit knowledge. In practice, is The viewpoints’481'3 are grouped into 7 .'4#481'3
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